Detecting frames in news headlines and lead images in U.S. gun violence coverage

Isidora Chara Tourni; Taufiq Daryanto; Fabian Zhafransyah; Lei Guo; Edward Halim; Mona Jalal; Boqi Chen; Sha Lai; Hengchang Hu; Prakash Ishwar; Margrit Betke; Derry Tanti Wijaya

doi:10.18653/v1/2021.findings-emnlp.339

Detecting frames in news headlines and lead images in U.S. gun violence coverage

Isidora Chara Tourni, Taufiq Daryanto, Fabian Zhafransyah, Lei Guo, Edward Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

12 Citations (Scopus)

Abstract

News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called "frames"in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines. We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.

Original language	English
Title of host publication	Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021
Editors	Xuanjing Huang, Lucia Specia, Scott Wen-Tau Yih
Place of Publication	Stroudsburg PA USA
Publisher	Association for Computational Linguistics (ACL)
Pages	4037-4050
Number of pages	14
ISBN (Electronic)	9781955917100
DOIs	https://doi.org/10.18653/v1/2021.findings-emnlp.339
Publication status	Published - 2021
Externally published	Yes
Event	Empirical Methods in Natural Language Processing 2021 - Online, Punta Cana, Dominican Republic Duration: 7 Nov 2021 → 11 Nov 2021 https://2021.emnlp.org/ (Website) https://aclanthology.org/2021.emnlp-main.0/ (Proceedings) https://aclanthology.org/2021.findings-emnlp.0/ (Proceedings - findings)

Conference

Conference	Empirical Methods in Natural Language Processing 2021
Abbreviated title	EMNLP 2021
Country/Territory	Dominican Republic
City	Punta Cana
Period	7/11/21 → 11/11/21
Internet address	https://2021.emnlp.org/ (Website) https://aclanthology.org/2021.emnlp-main.0/ (Proceedings) https://aclanthology.org/2021.findings-emnlp.0/ (Proceedings - findings)

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10.18653/v1/2021.findings-emnlp.339

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Tourni, I. C., Daryanto, T., Zhafransyah, F., Guo, L., Halim, E., Jalal, M., Chen, B., Lai, S., Hu, H., Ishwar, P., Betke, M., & Wijaya, D. T. (2021). Detecting frames in news headlines and lead images in U.S. gun violence coverage. In X. Huang, L. Specia, & S. W.-T. Yih (Eds.), Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021 (pp. 4037-4050). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-emnlp.339

Tourni, Isidora Chara ; Daryanto, Taufiq ; Zhafransyah, Fabian et al. / Detecting frames in news headlines and lead images in U.S. gun violence coverage. Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021. editor / Xuanjing Huang ; Lucia Specia ; Scott Wen-Tau Yih. Stroudsburg PA USA : Association for Computational Linguistics (ACL), 2021. pp. 4037-4050

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title = "Detecting frames in news headlines and lead images in U.S. gun violence coverage",

abstract = "News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called {"}frames{"}in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines. We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.",

author = "Tourni, {Isidora Chara} and Taufiq Daryanto and Fabian Zhafransyah and Lei Guo and Edward Halim and Mona Jalal and Boqi Chen and Sha Lai and Hengchang Hu and Prakash Ishwar and Margrit Betke and Wijaya, {Derry Tanti}",

note = "Funding Information: This work is supported in part by the U.S. NSF grant 1838193, DARPA HR001118S0044 (the LwLL program), and the Department of the Air Force FA8750-19-2-3334 (Semi-supervised Learning of Multimodal Representations). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA, the Air Force, and the U.S. Government. Publisher Copyright: {\textcopyright} 2021 Association for Computational Linguistics.; Empirical Methods in Natural Language Processing 2021, EMNLP 2021 ; Conference date: 07-11-2021 Through 11-11-2021",

year = "2021",

doi = "10.18653/v1/2021.findings-emnlp.339",

language = "English",

pages = "4037--4050",

editor = "Xuanjing Huang and Lucia Specia and Yih, {Scott Wen-Tau}",

booktitle = "Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021",

publisher = "Association for Computational Linguistics (ACL)",

url = "https://2021.emnlp.org/, https://aclanthology.org/2021.emnlp-main.0/, https://aclanthology.org/2021.findings-emnlp.0/",

}

Tourni, IC, Daryanto, T, Zhafransyah, F, Guo, L, Halim, E, Jalal, M, Chen, B, Lai, S, Hu, H, Ishwar, P, Betke, M & Wijaya, DT 2021, Detecting frames in news headlines and lead images in U.S. gun violence coverage. in X Huang, L Specia & SW-T Yih (eds), Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021. Association for Computational Linguistics (ACL), Stroudsburg PA USA, pp. 4037-4050, Empirical Methods in Natural Language Processing 2021, Punta Cana, Dominican Republic, 7/11/21. https://doi.org/10.18653/v1/2021.findings-emnlp.339

Detecting frames in news headlines and lead images in U.S. gun violence coverage. / Tourni, Isidora Chara; Daryanto, Taufiq; Zhafransyah, Fabian et al.
Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021. ed. / Xuanjing Huang; Lucia Specia; Scott Wen-Tau Yih. Stroudsburg PA USA: Association for Computational Linguistics (ACL), 2021. p. 4037-4050.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Detecting frames in news headlines and lead images in U.S. gun violence coverage

AU - Tourni, Isidora Chara

AU - Daryanto, Taufiq

AU - Zhafransyah, Fabian

AU - Guo, Lei

AU - Halim, Edward

AU - Jalal, Mona

AU - Chen, Boqi

AU - Lai, Sha

AU - Hu, Hengchang

AU - Ishwar, Prakash

AU - Betke, Margrit

AU - Wijaya, Derry Tanti

N1 - Funding Information: This work is supported in part by the U.S. NSF grant 1838193, DARPA HR001118S0044 (the LwLL program), and the Department of the Air Force FA8750-19-2-3334 (Semi-supervised Learning of Multimodal Representations). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA, the Air Force, and the U.S. Government. Publisher Copyright: © 2021 Association for Computational Linguistics.

PY - 2021

Y1 - 2021

N2 - News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called "frames"in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines. We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.

AB - News media structure their reporting of events or issues using certain perspectives. When describing an incident involving gun violence, for example, some journalists may focus on mental health or gun regulation, while others may emphasize the discussion of gun rights. Such perspectives are called "frames"in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article. We observe that using multiple modes of information(article- and image-derived features) improves prediction of news frames over any single mode of information when the images are relevant to the frames of the headlines. We also observe that frame image relevance is related to the ease of conveying frames via images, which we call frame concreteness. Additionally, we release the first multimodal news framing dataset related to gun violence in the U.S., curated and annotated by communication researchers. The dataset will allow researchers to further examine the use of multiple information modalities for studying media framing.

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DO - 10.18653/v1/2021.findings-emnlp.339

M3 - Conference Paper

AN - SCOPUS:85129140514

SP - 4037

EP - 4050

BT - Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021

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ER -

Tourni IC, Daryanto T, Zhafransyah F, Guo L, Halim E, Jalal M et al. Detecting frames in news headlines and lead images in U.S. gun violence coverage. In Huang X, Specia L, Yih SWT, editors, Findings - Findings of the Association for Computational Linguistics - Findings of ACL: EMNLP 2021. Stroudsburg PA USA: Association for Computational Linguistics (ACL). 2021. p. 4037-4050 doi: 10.18653/v1/2021.findings-emnlp.339

Detecting frames in news headlines and lead images in U.S. gun violence coverage

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